System and method for diagnostic and treatment

ABSTRACT

A method may include obtaining first image data relating to a region of interest (ROI) of a first subject. The first image data corresponding to a first equivalent dose level may be acquired by a first device. The method may also include obtaining a model for denoising relating to the first image data and determining second image data corresponding to an equivalent dose level higher than the first equivalent dose level based on the first image data and the model for denoising. In some embodiments, the method may further include determining information relating to the ROI of the first subject based on the second image data and ecording the information relating to the ROI of the first subject.

CROSS REFERENCE TO RELATED APPLICATION

This present application is a continuation of U.S. application Ser. No.16/222,151 filed on Dec. 17, 2018, which is a continuation ofInternational Application No. PCT/CN2017/110005 filed on Nov. 8, 2017,the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure generally relates to a medical diagnostic andtreatment system, and more specifically relates to methods and systemsfor decreasing dosage in a radiotherapy procedure.

BACKGROUND

Various imaging techniques have been used in medical diagnosis,radiation therapy planning, surgery planning, and other medicalprocedures, such as X-ray photography, magnetic resonance imaging (MRI),computed tomography (CT), positron emission tomography (PET), etc. Forexample, the CT-based image-guided radiotherapy (IGRT) has been widelyused in radiation therapy.

Conventionally, a radiation therapy treatment plan (also referred to asa treatment plan) for a patient is generated before the treatmentstarts. The treatment plan may be delivered to the patient duringseveral treatment fractions spread over a treatment period of multipledays. During the treatment period, one or more IGRT images (e.g., a CTimage) may be used to determine and/or position a region of interest(ROI) (e.g., a cancer). However, an IGRT image generated based onlow-dose projection data may show noise and/or artifacts (e.g.,staircase artifacts). The artifacts may reduce the image quality andinfluence the results of diagnosis made on the basis of such an image. Ahigh-dose IGRT scan may at least partially alleviate these problems butat the cost of exposing a scanned patient to too more radiation. It isdesirable to provide systems and methods for generating a high-doseimage of improved quality, based on a low-dose scan.

SUMMARY

According to an aspect of the present disclosure, a method forprocessing image data is provided. The method may be implemented on atleast one machine each of which has at least one processor and storage.The method may include obtaining first image data relating to a regionof interest (ROI) of a first subject. The first image data maycorrespond to a first equivalent dose level. The method may furtherinclude obtaining a model for denoising relating to the first image dataand determining second image data corresponding to an equivalent doselevel higher than the first equivalent dose level based on the firstimage data and the model for denoising. In some embodiments, the methodmay include determining information relating to the ROI of the firstsubject based on the second image data and recording the informationrelating to the ROI of the first subject.

In some embodiments, the model for denoising may include a first neuralnetwork model for denoising. Multiple groups of training data relatingto multiple second subjects may be obtained. Each group of the multiplegroups of training data may relate to a second subject and each of themultiple groups of training data may include third image datacorresponding to a third equivalent dose level and fourth image datacorresponding to a fourth equivalent dose level lower than the thirdequivalent dose level. The first neural network model for denoising maybe obtained by training a neural network model based on the multiplegroups of training data.

In some embodiments, the model for denoising may include a first neuralnetwork model for denoising. Multiple groups of training data relatingto multiple second subjects may be obtained. Each group of the multiplegroups of training data may relate to a second subject and each of themultiple groups of training data may include third image datacorresponding to a third equivalent dose level and fourth image datacorresponding to a fourth equivalent dose level lower than the thirdequivalent dose level. A second neural network model for denoising maybe obtained by training a neural network model based on the multiplegroups of training data. Fifth image data relating to the first subjectmay be obtained. The fifth image data may correspond to a fifthequivalent dose level higher than the first equivalent dose level. Thefirst neural network model for denoising may be obtained by training thesecond neural network model for denoising based on the fifth image data.

In some embodiments, the first image data may be acquired by a firstdevice, and the fourth image data may be acquired by the first device.

In some embodiments, the first image data may be acquired by a firstdevice, and the fourth image data may be acquired by a second devicedifferent from the first device.

In some embodiments, at least one of the first image data or the fourthimage data may be preprocessed.

In some embodiments, the determining, based on the first image data andthe model for denoising, second image data corresponding to a secondequivalent dose level higher than the first equivalent dose level, mayfurther include determining, based on the model for denoising and thefirst image data, noise data included in the first image data; anddetermining, based on the noise data and the first image data, thesecond image data.

In some embodiments, the model for denoising may include an imagereconstruction model using an iterative reconstruction algorithm.

In some embodiments, the image reconstruction model may include a firststatistical model of noises in a projection domain. The first image datamay include first projection data. The first projection data may beprocessed based on the first statistical model of noises in theprojection domain to obtain second projection data. A first image may begenerated based on the second projection data. A second statisticalmodel of noises in an image domain may be generated based on the firststatistical model of noises. The second image data including a secondimage relating to the ROI of the subject may be determined based on thefirst image and the second statistical model of noises.

In some embodiments, the first image data may include first projectiondata. The second image data may include a target image relating to theROI of the first subject. Third projection data indicating a differencebetween the first projection data and second projection datacorresponding to an image estimation may be determined. Fourthprojection data may be determined based on the third projection data andthe first statistical model of noises. An error image relating to theROI of the first subject may be generated based on the fourth projectiondata. The target image relating to the ROI of the first subject may bedetermined based on the error image and a second statistical mode ofnoises.

In some embodiments, a value of an objective function in each iterationmay be determined iteratively based on the error image and the secondstatistical mode of noises. An image estimation after each iteration maybe updated based on the value of the objective function obtained in amost recent iteration. The target image may be determined until acondition is satisfied.

In some embodiments, the objective function may further include a firstregularization item for suppressing noises.

In some embodiments, the objective function may further include a secondregularization item for suppressing artifact. The second regularizationitem may be associated with sparsity of the first projection data.

In some embodiments, the first equivalent dose level may be no less than15% of the second equivalent dose level.

In some embodiments, the first equivalent dose level may be no less than50% of the second equivalent dose level.

In some embodiments, the first equivalent dose level may be no less than85% of the second equivalent dose level.

In some embodiments, the first image data may be acquired by a computedtomography (CT), and a ratio of the first equivalent dose level to thesecond equivalent dose level may be equal to 1:7.

In some embodiments, the first image data may be acquired by a cone beamcomputed tomography (CBCT), and a ratio of the first equivalent doselevel to the second equivalent dose level may be equal to 1:3.

In some embodiments, the first device may further include a radiotherapytreatment (RT) device.

According to an aspect of the present disclosure, a system forprocessing image data is provided. The system may include acomputer-readable storage medium storing executable instructions and atleast one processor in communication with the computer-readable storagemedium. When the executable instructions are executed, the executableinstructions may cause the system to implement a method. The method mayinclude obtaining first image data relating to a region of interest(ROI) of a first subject. The first image data may correspond to a firstequivalent dose level. The method may further include obtaining a modelfor denoising relating to the first image data and determining secondimage data corresponding to an equivalent dose level higher than thefirst equivalent dose level based on the first image data and the modelfor denoising. In some embodiments, the method may include determininginformation relating to the ROI of the first subject based on the secondimage data and recording the information relating to the ROI of thefirst subject.

According to another aspect of the present disclosure, a non-transitorycomputer readable medium is provided. The non-transitory computerreadable medium may include executable instructions. When theinstructions are executed by at least one processor, the instructionsmay cause the at least one processor to implement a method. The methodmay include obtaining first image data relating to a region of interest(ROI) of a first subject. The first image data may correspond to a firstequivalent dose level. The method may further include obtaining a modelfor denoising relating to the first image data and determining secondimage data corresponding to an equivalent dose level higher than thefirst equivalent dose level based on the first image data and the modelfor denoising. In some embodiments, the method may include determininginformation relating to the ROI of the first subject based on the secondimage data and recording the information relating to the ROI of thefirst subject.

According to an aspect of the present disclosure, a system forprocessing image data is provided. The system may include a dataacquisition module configured to obtain first image data relating to aregion of interest (ROI) of a first subject. The first image data maycorrespond to a first equivalent dose level. The system may furtherinclude a model generation module configured to obtain a model fordenoising relating to the first image data. The system may furtherinclude an image data processing module configured to determine secondimage data corresponding to an equivalent dose level higher than thefirst equivalent dose level based on the first image data and the modelfor denoising, determining information relating to the ROI of the firstsubject based on the second image data, and recording the informationrelating to the ROI of the first subject.

According to an aspect of the present disclosure, an image-guidedradiotherapy (IGRT) method is provided. The method may include obtainingfirst information relating to a region of interest (ROI) of a firstsubject from a treatment plan of the first subject; obtaining firstimage data relating to the region of interest (ROI) of the firstsubject, the first image data corresponding to a first equivalent doselevel; obtaining a model for denoising relating to the first image data;determining, based on the first image data and the model for denoising,second image data corresponding to an equivalent dose level higher thanthe first equivalent dose level; determining, based on the second imagedata, second information relating to the ROI of the first subject; andadjusting, based on a comparison between the second information relatingto the ROI and the first information relating to the ROI, a position ofthe subject in space.

In some embodiments, the model for denoising may include a first neuralnetwork model for denoising, and the obtaining a model for denoising,may further include obtaining multiple groups of training data relatingto multiple second subjects, each group of the multiple groups oftraining data relating to a second subject, each of the multiple groupsof training data including third image data corresponding to a thirdequivalent dose level and fourth image data corresponding to a fourthequivalent dose level lower than the third equivalent dose level;training, based on the multiple groups of training data, a neuralnetwork model to obtain a second neural network model for denoising;obtaining fifth image data relating to the first subject, the fifthimage data corresponding to a fifth equivalent dose level higher thanthe first equivalent dose level; and training, based on the fifth imagedata relating to the first subject, the second neural network model fordenoising to obtain the first neural network model for denoising.

In some embodiments, the model for denoising may include an imagereconstruction model using an iterative reconstruction algorithm.

According to an aspect of the present disclosure, a method for radiationdelivery is provided. The method may include obtaining first image datarelating to a region of interest (ROI) of a first subject before orduring or after a treatment, the first image data corresponding to afirst equivalent dose level; obtaining a model for denoising relating tothe first image data; determining, based on the first image data and themodel for denoising, second image data corresponding to an equivalentdose level higher than the first equivalent dose level; determining,based on the second image data, information relating to the ROI of thefirst subject; and modifying, based on a comparison between thedetermined information relating to the ROI and information relating tothe ROI in a treatment plan of the first subject, the treatment plan ofthe first subject.

According to an aspect of the present disclosure, a method for radiationdelivery is provided. The method may include obtaining first image datarelating to a region of interest (ROI) of a first subject before orduring or after a treatment, the first image data corresponding to afirst equivalent dose level; obtaining a model for denoising relating tothe first image data; determining, based on the first image data and themodel for denoising, second image data corresponding to an equivalentdose level higher than the first equivalent dose level; determining,based on the second image data, first information relating to the ROI ofthe first subject; comparing the first information relating to the ROIof the first subject and second information relating to the ROI in atreatment plan of the first subject; and performing a delivery oftreatment radiation beam based on the comparison, including at least oneof deactivating a delivery of treatment radiation beam in response to adetermination that the comparison is outside a range; or reactivating adelivery of treatment radiation beam in response to a determination thatthe comparison is within the range.

According to an aspect of the present disclosure, a planning method fora treatment is provided. The method may include obtaining first imagedata relating to a region of interest (ROI) of a subject, the firstimage data corresponding to a first equivalent dose level; obtaining amodel for denoising relating to the first image data; determining, basedon the first image data and the model for denoising, second image datacorresponding to an equivalent dose level higher than the firstequivalent dose level; determining, based on the second image data,information relating to the ROI of the first subject; and determining,based on the information relating to the ROI, a treatment plan of thesubject.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary diagnostic andtreatment system according to some embodiments of the presentdisclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device on which theprocessing device may be implemented according to some embodiments ofthe present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device on which theterminal(s) may be implemented according to some embodiments of thepresent disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 5 is a block diagram illustrating an exemplary image dataprocessing module according to some embodiments of the presentdisclosure;

FIG. 6 is a flowchart illustrating an exemplary process for processingimage data according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for processinglow-dose image data based on a neural network model for denoisingaccording to some embodiments of the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for determininga neural network model for denoising according to some embodiments ofthe present disclosure;

FIG. 9 is a flowchart illustrating an exemplary process for processinglow-dose image data based on a statistical model of noises according tosome embodiments of the present disclosure;

FIG. 10 is a flowchart illustrating an exemplary process for processinglow-dose image data based on an iterative reconstruction techniqueaccording to some embodiments of the present disclosure;

FIG. 11 is a flowchart illustrating an exemplary process for processinglow-dose image data based on an iterative reconstruction techniqueaccording to some embodiments of the present disclosure;

FIG. 12 is a schematic diagram illustrating an exemplary convolutionalneural network (CNN) model according to some embodiments of the presentdisclosure;

FIG. 13A and FIG. 13B illustrate exemplary images corresponding todifferent dose levels according to some embodiments of the presentdisclosure;

FIGS. 14A-14C illustrate exemplary images corresponding to differentdose levels according to some embodiments of the present disclosure;

FIGS. 15A-15C illustrate exemplary images corresponding to differentdose levels generated based on different reconstruction algorithmsaccording to some embodiments of the present disclosure;

FIGS. 16A and 16B illustrate exemplary images corresponding to the samedose level generated based on different reconstruction algorithmsaccording to some embodiments of the present disclosure;

FIGS. 17A-17C illustrate exemplary images corresponding to the same doselevel generated based on different reconstruction algorithms accordingto some embodiments of the present disclosure;

FIGS. 18A and 18B illustrate exemplary images corresponding to the samedose level generated based on different reconstruction algorithmsaccording to some embodiments of the present disclosure; and

FIGS. 19A and 19B illustrate exemplary images corresponding to the samedose level generated based on different reconstruction algorithmsaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by anotherexpression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or themselves,and/or may be invoked in response to detected events or interrupts.Software modules/units/blocks configured for execution on computingdevices (e.g., processor 210 as illustrated in FIG. 2) may be providedon a computer-readable medium, such as a compact disc, a digital videodisc, a flash drive, a magnetic disc, or any other tangible medium, oras a digital download (and can be originally stored in a compressed orinstallable format that needs installation, decompression, or decryptionprior to execution). Such software code may be stored, partially orfully, on a storage device of the executing computing device, forexecution by the computing device. Software instructions may be embeddedin firmware, such as an EPROM. It will be further appreciated thathardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description mayapply to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments of the presentdisclosure. It is to be expressly understood, the operations of theflowcharts may be implemented not in order. Conversely, the operationsmay be implemented in inverted order, or simultaneously. Moreover, oneor more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

Provided herein are systems and components for medical diagnostic and/ortreatment. In some embodiments, the medical system may include adiagnostic and treatment system. The diagnostic and treatment system mayinclude a treatment plan system (TPS), an image-guide radio therapy(IGRT) system (e.g., an CT guided radiotherapy system), etc.

An aspect of the present disclosure relates to a system and method forprocessing image data. The system may process first image data relatingto an ROI of a subject corresponding to a first equivalent dose levelbased on a model for denoising to obtain second image data. The secondimage data may correspond to an equivalent dose level higher than thefirst equivalent dose level. In some embodiments, the model fordenoising may include a neural network model for denoising. Further, thefirst image data may be processed based on the neural network model fordenoising to obtain noise data. The second image data may be determinedbased on the first image data and the noise data. In some embodiments,the model for denoising may include an iterative reconstruction model.The second image data may be determined based on the iterativereconstruction model using an iterative reconstruction algorithm.

It should be noted that the diagnostic and treatment system 100described below is merely provided for illustration purposes, and notintended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, a certain amount of variations,changes, and/or modifications may be deducted under the guidance of thepresent disclosure. Those variations, changes, and/or modifications donot depart from the scope of the present disclosure.

FIG. 1 is a schematic diagram illustrating an exemplary diagnostic andtreatment system 100 according to some embodiments of the presentdisclosure. As shown, the diagnostic and treatment system 100 mayinclude an image guided radio therapy (IGRT) apparatus 110, a processingdevice 120, storage 130, one or more terminal(s) 140, and a network 150.In some embodiments, the IGRT apparatus 110, the processing device 120,the storage 130, and/or the terminal(s) 140 may be connected to and/orcommunicate with each other via a wireless connection (e.g., the network150), a wired connection, or a combination thereof. The connectionsbetween the components in the diagnostic and treatment system 100 mayvary. Merely by way of example, the IGRT apparatus 110 may be connectedto the processing device 120 through the network 150, as illustrated inFIG. 1. As another example, the IGRT apparatus 110 may be connected tothe processing device 120 directly. As a further example, the storage130 may be connected to the processing device 120 through the network150, as illustrated in FIG. 1, or connected to the processing device 120directly. As still a further example, the terminal(s) 140 may beconnected to the processing device 120 through the network 150, asillustrated in FIG. 1, or connected to the processing device 120directly (as indicated by the bidirectional arrow in dashed line shownin FIG. 1).

The IGRT apparatus 110 may be a multi-modality (e.g., two-modality)apparatus to acquire a medical image relating to at least one part of asubject and perform radiotherapy treatment on the at least one part ofthe subject. In some embodiments, the medical image may be atwo-dimensional (2D) image, a three-dimensional (3D) image, afour-dimensional (4D) image, or the like, or a combination thereof. Thesubject may be biological or non-biological. For example, the subjectmay include a patient, a man-made object, etc. As another example, thesubject may include a specific portion, organ, and/or tissue of thepatient. For example, the subject may include head, neck, thorax,cardiac, stomach, blood vessel, soft tissue, tumor, nodules, or thelike, or a combination thereof. In some embodiments, the subject mayinclude a region of interest (ROI), such as a tumor, a node, etc.

In some embodiments, the IGRT apparatus 110 may include an imagingdevice 112, a treatment device 114, and a couch 116. The imaging device112 may be configured to provide image data via scanning a subject, or apart of the subject. In some embodiments, the imaging device 112 mayinclude a single-modality scanner and/or multi-modality scanner. Thesingle-modality may include, for example, a computed tomography (CT)scanner, a cone beam CT (CBCT), etc. The multi-modality scanner mayinclude a single photon emission computed tomography-computed tomography(SPECT-CT) scanner, a positron emission tomography-computed tomography(PET-CT) scanner, a computed tomography-ultra-sonic (CT-US) scanner, adigital subtraction angiography-computed tomography (DSA-CT) scanner, acomputed tomography-magnetic resonance (CT-MR) scanner, or the like, ora combination thereof. In some embodiments, the image data may includeprojection data, images relating to the subject, etc. The projectiondata may be raw data generated by the imaging device 112 by scanning thesubject, or data generated by a forward projection on an image relatingto the subject.

In some embodiments, the imaging device 112 may include an imagingradiation source, a detector, etc. The imaging radiation source maygenerate and/or emit one or more radiation beams toward the subjectaccording to one or more scanning parameters. The detector of theimaging device 112 may include one or more detector units that maydetect a distribution of the radiation beams emitted from the imagingradiation source. In some embodiments, the detector of the imagingdevice 112 may be connected to a data conversation circuit configured toconvert the distribution of the detected radiation beams into image data(e.g., projection data). The image data may correspond to the dose levelof a detected radiation beams. In some embodiments, the dose level ofthe detected radiation beams may include noise represented in the imagedata. For example, the higher the dose level of radiation is, the lowerthe noise level relative to true signal (reflecting actual anatomy)represented in the image data may be. The lower the dose-level ofradiation is, the higher the noise level relative to true signalrepresented in the image data may be. As used herein, the dose level ofthe radiation may be defined by a CT dose index (CTDI), an effectivedose, a dose-length product, etc. The CT dose index (CTDI) may refer tothe radiation energy of radiation corresponding to a single slice alonga long axis (e.g., the axial direction) of the imaging device 112. Thedose-length product may refer to the total radiation energy of radiationreceived by a subject being examined in an integrated scanningprocedure. The effective dose may refer to the radiation energy ofradiation received by a specific region of a subject in an integratedscanning procedure.

The treatment device 114 may be configured to perform radiotherapy on atleast one part of the subject (e.g., an ROI) according to the medicalimage and other information. The treatment device 114 may include atreatment radiation source. The treatment radiation source may emittreatment radiations towards the subject. Exemplary treatment devicesmay include a linear accelerator, an X-ray treatment device, etc. Thecouch 116 may be configured to support and/or transfer the at least onepart of the subject to for example, a scanning region of the imagingdevice 112 and/or the treatment device 114. For example, the couch 116may be moved to transfer the subject from the imaging device 112 to thetreatment device 114.

In some embodiments, the IGRT apparatus 110 may include two gantriesthat house the imaging device 112 and the treatment device 114,respectively. The imaging device 112 and the corresponding gantry may bespaced by a distance from the treatment device 114 and the correspondinggantry. In some embodiments, the corresponding gantry of the imagingdevice 112 and the corresponding gantry of the treatment device 114 mayhave collinear bores. For example, a bore of the gantry of the imagingdevice 112 and a bore of the gantry of the treatment device 114 mayshare an axis of rotation. The subject may be positioned in differentpositions in the table 116 for imaging and treatment. In someembodiments, the imaging radiation source of the imaging device 112 andthe treatment radiation source of the treatment device 114 may beintegrated as one radiation source to imaging and/or treat the subject.Merely by way of example, the IGRT apparatus 110 may include a treatmentdevice and a CT scanner. Descriptions of such a device may be found in,e.g., US Publication Nos. 20170189720A1 and 20170189724A1, both entitled“Radiation therapy system,” and US Publication No. 20170189719A1entitled “Radiation therapy positioning system,” the contents of each ofwhich are hereby incorporated by reference.

The processing device 120 may process data and/or information obtainedfrom the IGRT apparatus 110, the storage 130, and/or the terminal(s)140. For example, the processing device 120 may reconstruct an imagerelating to at least one part of a subject (e.g., a tumor) based onprojection data collected by the IGRT apparatus 110 (e.g., the imagingdevice 112). As another example, the processing device 120 may determineone or more neural network models for denoising configured to processand/or convert image data. As a further example, the processing device120 may determine a treatment plan based on at least one part of thesubject (e.g., a tumor) represented in an image acquired by the imagingdevice 112.

In some embodiments, the processing device 120 may be a single server ora server group. The server group may be centralized or distributed. Insome embodiments, the processing device 120 may be local or remote. Forexample, the processing device 120 may access information and/or datafrom the IGRT apparatus 110, the storage 130, and/or the terminal(s) 140via the network 150. As another example, the processing device 120 maybe directly connected to the IGRT apparatus 110, the terminal(s) 140,and/or the storage 130 to access information and/or data. In someembodiments, the processing device 120 may be implemented on a cloudplatform. For example, the cloud platform may include a private cloud, apublic cloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or a combination thereof. Insome embodiments, the processing device 120 may be implemented by amobile device 300 having one or more components as described inconnection with FIG. 3.

The storage 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage 130 may store dataobtained from the IGRT apparatus 110, the processing device 120, and/orthe terminal(s) 140. In some embodiments, the storage 130 may store dataand/or instructions that the processing device 120 may execute or use toperform exemplary methods described in the present disclosure. In someembodiments, the storage 130 may include a mass storage, a removablestorage, a volatile read-and-write memory, a read-only memory (ROM), orthe like, or any combination thereof. Exemplary mass storage may includea magnetic disk, an optical disk, a solid-state drive, etc. Exemplaryremovable storage may include a flash drive, a floppy disk, an opticaldisk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (EPROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage 130 may be implemented on a cloudplatform as described elsewhere in the disclosure.

In some embodiments, the storage 130 may be connected to the network 150to communicate with one or more other components in the diagnostic andtreatment system 100 (e.g., the processing device 120, the terminal(s)140, etc.). One or more components in the diagnostic and treatmentsystem 100 may access the data or instructions stored in the storage 130via the network 150. In some embodiments, the storage 130 may be part ofthe processing device 120.

The terminal(s) 140 may be connected to and/or communicate with the IGRTapparatus 110, the processing device 120, and/or the storage 130. Forexample, the terminal(s) 140 may obtain a processed image from theprocessing device 120. As another example, the terminal(s) 140 mayobtain image data acquired via the IGRT apparatus 110 and transmit theimage data to the processing device 120 to be processed. In someembodiments, the terminal(s) 140 may include a mobile device 140-1, atablet computer 140-2, . . . , a laptop computer 140-N, or the like, orany combination thereof. For example, the mobile device 140-1 mayinclude a mobile phone, a personal digital assistance (PDA), a gamingdevice, a navigation device, a point of sale (POS) device, a laptop, atablet computer, a desktop, or the like, or any combination thereof. Insome embodiments, the terminal(s) 140 may include an input device, anoutput device, etc. The input device may include alphanumeric and otherkeys that may be input via a keyboard, a touch screen (for example, withhaptics or tactile feedback), a speech input, an eye tracking input, abrain monitoring system, or any other comparable input mechanism. Theinput information received through the input device may be transmittedto the processing device 120 via, for example, a bus, for furtherprocessing. Other types of the input device may include a cursor controldevice, such as a mouse, a trackball, or cursor direction keys, etc. Theoutput device may include a display, a speaker, a printer, or the like,or a combination thereof. In some embodiments, the terminal(s) 140 maybe part of the processing device 120.

The network 150 may include any suitable network that can facilitateexchange of information and/or data for the diagnostic and treatmentsystem 100. In some embodiments, one or more components of thediagnostic and treatment system 100 (e.g., the IGRT apparatus 110, theprocessing device 120, the storage 130, the terminal(s) 140, etc.) maycommunicate information and/or data with one or more other components ofthe diagnostic and treatment system 100 via the network 150. Forexample, the processing device 120 may obtain image data from the IGRTapparatus 110 via the network 150. As another example, the processingdevice 120 may obtain user instruction(s) from the terminal(s) 140 viathe network 150. The network 150 may be and/or include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN), a wide area network (WAN)), etc.), a wired network (e.g., anEthernet network), a wireless network (e.g., an 802.11 network, a Wi-Finetwork, etc.), a cellular network (e.g., a Long Term Evolution (LTE)network), a frame relay network, a virtual private network (VPN), asatellite network, a telephone network, routers, hubs, witches, servercomputers, and/or any combination thereof. For example, the network 150may include a cable network, a wireline network, a fiber-optic network,a telecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or any combination thereof. Insome embodiments, the network 150 may include one or more network accesspoints. For example, the network 150 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the diagnostic andtreatment system 100 may be connected to the network 150 to exchangedata and/or information.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, thestorage 130 may be a data storage including cloud computing platforms,such as, public cloud, private cloud, community, and hybrid clouds, etc.As another example, the diagnostic and treatment system 100 may furtherinclude a treatment planning system. However, those variations andmodifications do not depart the scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device 200 on which theprocessing device 120 may be implemented according to some embodimentsof the present disclosure. As illustrated in FIG. 2, the computingdevice 200 may include a processor 210, storage 220, an input/output(I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process image dataobtained from the IGRT device 110, the storage 130, terminal(s) 140,and/or any other component of the diagnostic and treatment system 100.In some embodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or a combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the IGRT device110, the storage 130, the terminal(s) 140, and/or any other component ofthe diagnostic and treatment system 100. In some embodiments, thestorage 220 may include a mass storage, removable storage, a volatileread-and-write memory, a read-only memory (ROM), or the like, or acombination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (EPROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store a program for the processing device120 for determining a target flip angle schedule.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. Examples of the input device mayinclude a keyboard, a mouse, a touch screen, a microphone, or the like,or a combination thereof. Examples of the output device may include adisplay device, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Examples of the display device may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), a touch screen, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and theIGRT apparatus 110, the storage 130, and/or the terminal(s) 140. Theconnection may be a wired connection, a wireless connection, any othercommunication connection that can enable data transmission and/orreception, and/or a combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or a combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee link, a mobile networklink (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof. Insome embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 on which theterminal(s) 140 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 3, the mobile device 300 mayinclude a communication platform 410, a display 320, a graphicprocessing unit (GPU) 330, a central processing unit (CPU) 340, an I/O350, a memory 360, and a storage 390. In some embodiments, any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 300.In some embodiments, a mobile operating system 370 (e.g., iOS™,Android™, Windows Phone™, etc.) and one or more applications 380 may beloaded into the memory 360 from the storage 390 in order to be executedby the CPU 340. The applications 380 may include a browser or any othersuitable mobile apps for receiving and rendering information relating toimage processing or other information from the processing device 120.User interactions with the information stream may be achieved via theI/O 350 and provided to the processing device 120 and/or othercomponents of the diagnostic and treatment system 100 via the network150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIG. 4 is a block diagram illustrating an exemplary processing device120 according to some embodiments of the present disclosure. Theprocessing device 120 may include an acquisition module 402, a controlmodule 404, an image data processing module 406, and a storage module408. At least a portion of the processing device 120 may be implementedon a computing device as illustrated in FIG. 2 or a mobile device asillustrated in FIG. 3.

The acquisition module 402 may acquire data. In some embodiments, thedata may be acquired from the IGRT apparatus 110, the storage 130,and/or the terminal(s) 140. In some embodiments, the data may includeimage data (e.g., a radiological image, projection data, etc.), models,instructions, or the like, or a combination thereof. The models may beused to generate an image. The instructions may be executed by theprocessor(s) of the processing device 120 to perform exemplary methodsdescribed in the present disclosure. In some embodiments, the acquireddata may be transmitted to the image data processing module 406 forfurther processing, or stored in the storage module 408.

The control module 404 may control operations of the acquisition module402, the image data processing module 406, and/or the storage module408, for example, by generating one or more control parameters. Forexample, the control module 404 may control the acquisition module 402to acquire image data (e.g., an image, projection data, etc.) from theimaging device 112 of the IGRT apparatus 110. As another example, thecontrol module 404 may control the image data processing module 406 togenerate an image relating to a subject. As a further example, thecontrol module 404 may control the image data processing module 406 todetermine a radiotherapy treatment of the subject based on the image. Insome embodiments, the control module 404 may receive a real-time commandor retrieve a predetermined instruction provided by a user (e.g., adoctor) to control one or more operations of the acquisition module 402and/or the image data processing module 406. For example, the controlmodule 404 may adjust the acquisition module 402 and/or the image dataprocessing module 406 to generate image data (e.g., an image) accordingto the real-time instruction and/or the predetermined instruction. Insome embodiments, the control module 404 may communicate with one ormore other modules of the processing device 120 for exchanginginformation and/or data.

The image data processing module 406 may process data provided byvarious modules of the processing device 120. In some embodiments, theimage data processing module 406 may generate high-dose image data basedon low-dose image data. For example, the image data processing module406 may generate high-dose image data using a neural network model fordenoising. As another example, the image data processing module 406 maygenerate high-dose image data using an iterative reconstructiontechnique based on a statistical model of noises. In some embodiments,the image data processing module 406 may determine a radiotherapytreatment based on the high-dose image data.

The storage module 408 may store information. The information mayinclude programs, software, algorithms, data, text, number, images andsome other information. For example, the information may include imagedata (e.g., a radiological image, an optical image, etc.), motion orposition data (e.g., a speed, a displacement, an acceleration, a spatialposition, etc.) relating to a component in the IGRT apparatus 110 (e.g.,the couch 116), instructions, or the like, or a combination thereof. Insome embodiments, the storage module 408 may store program(s) and/orinstruction(s) that can be executed by the processor(s) of theprocessing device 120 to acquire data, determine a spatial position ofat least one part of a subject.

In some embodiments, one or more modules illustrated in FIG. 5 may beimplemented in at least part of the diagnostic and treatment system 100as illustrated in FIG. 1. For example, the acquisition module 402, thecontrol module 404, the image data processing module 406, and/or thestorage module 408 may be integrated into a console (not shown). Via theconsole, a user may set parameters for scanning a subject, controllingimaging processes, controlling parameters for reconstruction of animage, etc. In some embodiments, the console may be implemented via theprocessing device 120 and/or the terminal(s) 140.

FIG. 5 is a block diagram illustrating an exemplary image dataprocessing module 406 according to some embodiments of the presentdisclosure. The image data processing module 406 may include a neuralnetwork model determination unit 502, an image denoising unit 504, aniterative reconstruction unit 506, and a storage unit 508. At least aportion of the image data processing module 406 may be implemented on acomputing device as illustrated in FIG. 2 or a mobile device asillustrated in FIG. 3.

The neural network model determination unit 502 may be configured togenerate a neural network model for denoising. In some embodiments, theneural network model determination unit 502 may generate a generalnetwork model for denoising via training a neural network model based onmultiple groups of training data relating to multiple differentsubjects. In some embodiments, the neural network model determinationunit 502 may train the general neural network model for denoising basedon training data relating to a specified subject to obtain apersonalized neural network model for denoising.

In some embodiments, the neural network model determination unit 502 maytransmit the neural network model for denoising to other units or blocksof the image data processing module 406 for further processing. Forexample, the neural network model determination unit 502 may transmitthe neural network model for denoising to the image data denoising unit504 for processing image data. As another example, the neural networkmodel determination unit 502 may transmit the neural network model fordenoising to the storage unit 508 for storage.

The image data denoising unit 504 may be configured to denoise imagedata. For example, the image data denoising unit 504 may convertlow-dose image data to high-dose image data using a neural network modelfor denoising determined by, for example, the neural network modeldetermination unit 502. As another example, the image data denoisingunit 504 may determine noise data included in low-dose image data usinga neural network model for denoising, and determine high-dose image datacorresponding to the low-dose image data based on the noise data and thelow-dose image data.

The iterative reconstruction unit 506 may be configured to generate ahigh-dose image based on corresponding low-dose projection data and astatistical model of noises by performing a plurality of iterations. Insome embodiments, the iterative reconstruction unit 506 may generate thestatistical model of noises relating to the low-dose projection data. Insome embodiments, the iterative reconstruction unit 506 may denoise thelow-dose projection data based on the statistical model of noises, andreconstruct an image based on the denoised low-dose projection data. Insome embodiments, the iterative reconstruction unit 506 may denoise theimage based on the statistical model of noises to obtain the high-doseimage corresponding to the low-dose projection data.

The storage unit 508 may store information relating to, for example,training a neural network model, a statistical model of noises, etc. Theinformation may include programs, software, algorithms, data, text,number, and some other information. In some embodiments, the informationrelating to training a neural network model may include images fortraining a neural network model, algorithms for training a neuralnetwork model, parameters of a neural network model, etc. The storageunit 580 may be a memory that stores data to be processed by processingdevices, such as CPUs, GPUs, etc. In some embodiments, the storage unit508 may be a memory that may be accessible by one or more GPUs, or maybe memory that is only accessible by a specific GPU.

It should be noted that the above description of the processing module430 is merely provided for the purposes of illustration, and notintended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations or modificationsmay be made under the teachings of the present disclosure. However,those variations and modifications do not depart from the scope of thepresent disclosure. For example, the neural network model determinationunit 502 and the image data denoising unit 504 may be integrated intoone single unit.

FIG. 6 is a flowchart illustrating an exemplary process 600 forprocessing image data according to some embodiments of the presentdisclosure. In some embodiments, one or more operations of process 600illustrated in FIG. 6 may be implemented in the diagnostic and treatmentsystem 100 illustrated in FIG. 1. For example, the process 600illustrated in FIG. 6 may be stored in the storage 130 in the form ofinstructions, and invoked and/or executed by the processing device 120(e.g., the processor 210 of the computing device 200 as illustrated inFIG. 2, the GPU 330 or CPU 340 of the mobile device 300 as illustratedin FIG. 3).

In 602, first image data corresponding to a first equivalent dose leveland relating to the ROI of the subject may be obtained. Operation 602may be performed by the acquisition module 402. The term “equivalentdose level” may also refer to the radiation energy of radiation receivedby a unit mass of a subject in a scanning procedure when simulatingdifferent numbers of photons. In some embodiments, the equivalent doselevel may equal to a scanning dose level relating to the subject. Insome embodiments, the first image data may obtained from the storage130, the storage module 408, or any other external storage. In someembodiments, the first image data may be obtained from an imaging deviceof an IGRT apparatus (e.g., the imaging device 112 of the IGRT apparatus110) generated by scanning the subject at the first equivalent doselevel. The first image data may include projection data, an imagerelating to the ROI of the subject, or the like, or a combinationthereof. The first image data may include two-dimensional (2D) imagedata, three-dimensional (3D) image data, four-dimensional (4D) imagedata, or image data of other dimensions.

In some embodiments, the first image data may include planning imagedata (e.g., a planning image, planning projection data, etc.) relatingto the ROI of the subject. As used herein, planning image data may beused to design a treatment plan of the subject. For example, before thesubject receives a radiation therapy (e.g., days or weeks before),planning image may be taken. The planning image may be used to identifya radiotherapy target (e.g., the ROI of the subject), an organ at risk,and the external contour (e.g., skin) of the subject, and the treatmentplan may be designed for the subject based on the planning image. Insome embodiments, the first image data may include guiding image data(e.g., a guiding image, guiding projection data, etc.) relating to theROI of the subject. As used herein, guiding image data may be used toguide the implementation of the treatment plan. For example, the guidingimage relating to the ROI of the subject may be used to position theROI. The positioned ROI may receive radiation according to the treatmentplan. The guiding image data may be taken during or before the radiationtherapy (e.g., on the day of treatment, or hours before the treatment,or minutes before the treatment, or seconds before the treatment, orduring the treatment). In some embodiments, the treatment plan may bedelivered to the subject during several treatment fractions spread overa treatment period of multiple days. During the treatment period, one ormore guiding images (e.g., a CT image) may be used to determine and/orposition the ROI (e.g., a cancer) of the subject during the severaltreatment fractions.

The first equivalent dose level of the first image data may satisfy afirst condition. In some embodiments, the first condition may include afirst threshold or a first range. For example, the first equivalent doselevel may be equal to or lower than the first threshold. As anotherexample, the first equivalent dose level may be in the first range. Insome embodiments, the first condition (e.g., the first threshold or thefirst range) may vary according to clinical demands (e.g., a type of atissue of interest). For example, in a liver scan, the first equivalentdose level may be equal to or lower than 15 mSv, or 10 mSv, or 5 mSv,etc. As another example, in a chest scan, the first equivalent doselevel may be equal to or lower than 7 mSv, or 2 mSv, etc. As still anexample, in an epigastrium scan with a CBCT device, the first equivalentdose level may be equal to 4 mGy. In an epigastrium scan with a CTdevice under scanning parameters 120 kv and 30 mAs, the first equivalentdose level may be equal to 1.5 mGy.

In 604, a model for denoising may be obtained. Operation 604 may beperformed by the acquisition module 402. In some embodiments, the modelfor denoising may be pre-determined (e.g., provided by a manufacturer ofan IGRT apparatus, an entity specializing in image processing, an entityhaving access to training data, etc.). In some embodiments, the modelfor denoising may be obtained from the storage 130, the storage module408, the storage unit 508, the terminal(s) 140, or other storagedevices.

In some embodiments, the model for denoising may include a neuralnetwork model for denoising. The neural network model for denoising maybe configured to process image data (e.g., the first image data obtainedin 602). Exemplary image data processing may include transform,modification, and/or conversion, etc. For example, the neural networkmodel for denoising may be configured to convert low-dose image data(e.g., the first image data obtained in 602) to high-dose image data(e.g., the second image data determined in 608) corresponding to thelow-dose image data. As another example, the neural network model fordenoising may be configured to reduce the noise level in image data(e.g., the first image data obtained in 602). As still an example, theneural network model for denoising may extract noise data from imagedata (e.g., the first image data obtained in 602). In some embodiments,the neural network model for denoising may include a general neuralnetwork model for denoising generated based on training data acquiredfrom multiple objects. In some embodiments, the neural network model fordenoising may include a personalized neural network model correspondingto the subject. More descriptions of the neural network model fordenoising may be found in FIG. 7 and FIG. 8 and the descriptionsthereof.

In some embodiments, the model for denoising may include a statisticalmodel of noises. The statistical model of noises may represent the noiselevel of image data (e.g., the first image data obtained in 602). Insome embodiments, the statistical model of noises may be constructedbased on a spatial-domain filter model, a transform-domain filter model,a morphological noise filter model, or the like, or a combinationthereof. The spatial-domain filter model may include a field averagefilter model, a median filter model, a Gaussian filter model, or thelike, or a combination thereof. The transform-domain filter model mayinclude a Fourier transform model, a Walsh-Hadamard transform model, acosine transform model, a K-L transform model, a wavelet transformmodel, or the like, or a combination thereof. In some embodiments, thestatistical model of noises may include a partial differential model ora variational model, such as a Perona-Malik (P-M) model, a totalvariation (TV) model, or the like, or a combination thereof. In someembodiments, the statistical model of noises may include a noiseestimation of the first image data. The noise estimation of the firstprojection data may represent the noise level of the first image data.In some embodiments, noises included in the first image data may includequantum noises incurred by radiation (e.g., X-rays), electronic noisesincurred by a component of an imaging device (e.g., the detector in theimaging device 112), or the like, or a combination thereof. The noiseestimation of the first image data may be determined based on thequantum noises, the electronic noises, and an intensity of radiationdetected by a detector (e.g., the detector in the imaging device 112).More descriptions of the statistical model of noises may be found in,for example, Chinese Publication No 103971387B, entitled “SYSTEM ANDMETHOD FOR CT IMAGE RECONSTRUCTION,” the contents of which are herebyincorporated by reference.

In 606, the first image data may be preprocessed. Operation 606 may beperformed by the image data processing module 406. The preprocessingoperation may be performed to adjust the quality of image data, such asthe noise level of image data, the contrast ratio of an image, etc. Insome embodiments, the preprocessing operation may include a denoisingoperation, an enhancement operation, a smoothing operation, an imagefusion operation, an image segmentation operation, an image registrationoperation, or the like, or a combination thereof. Specifically, thesmoothing operation may be performed based on a Gaussian filter, anaverage filter, a median filter, a wavelet transformation, or the like,or a combination thereof. The enhancing operation may include ahistogram equalization, an image sharpening, a Fourier transform, ahigh-pass filtering, a low-pass filtering, or the like, or a combinationthereof. The denoising operation may include applying a spatial-domainfilter, a transform-domain filter, a morphological noise filter, or thelike, or a combination thereof. The image segmentation operation may beperformed based on a segmentation algorithm. Exemplary segmentationalgorithms may include a threshold-based segmentation algorithm, anedge-based segmentation algorithm, a region-based segmentationalgorithm, or the like, or a combination thereof. The image fusionoperation may be performed using, for example, an optimal seam-linealgorithm, a gradient pyramid algorithm, etc. The image registrationoperation may be performed using, for example, a cross-correlationalgorithm, a Walsh transform algorithm, a phase correlation algorithm,etc.

In 608, second image data corresponding to a second equivalent doselevel higher than the first equivalent dose level may be determinedbased on the first image data and the model for denoising. Operation 608may be performed by the image data processing module 406. In someembodiments, the second image data may include projection data, animage, or the like, or a combination thereof. The second equivalent doselevel may be an equivalent dose level of the first image data relativeto the first equivalent dose level. As used herein, the secondequivalent dose level may refer to a dose level required by the firstimage data when the noise level of the first image data is equal to thatof the second image data. The equivalent dose level may also refer tothe radiation energy of radiation received by a unit mass of a subjectin a scanning procedure when simulating different numbers of photons. Aratio of the first equivalent dose level to the second equivalent doselevel may be equal to or exceed 10%, or 15%, or 25%, or 30%, or 40%, or50%, 85%, etc. In an epigastrium scan, a ratio of the first equivalentdose level to the second equivalent dose level may range from equal toor exceed 10%, or 15%, or 25%, or 30%, or 40%, or 50%, 85%, etc. Forexample, for a CBCT scan, the ratio of the first equivalent dose levelto the-second equivalent dose level may equals to or exceeds 1:3, or1:2. For a CT scan, the ratio of the first equivalent dose level tothe-second equivalent dose level may equal to 1:7, or 1:5.

The second image data may be determined based on the model for denoisingobtained in 604. For example, the second image data may be determinedbased on the neural network model for denoising. Further, the secondimage data may be determined via converting the first image data intothe second image data directly using the neural network model fordenoising. As another example, the first image data may be convertedinto noise data using the neural network model for denoising. Then, thesecond image data may be determined based on the noise data and thefirst image data according to process 800 as illustrated in FIG. 8. Asanother example, the second image data may be determined based on thestatistical model of noises using an iterative reconstruction technique.For example, the first image data may include first projection data. Thesecond image data may include a second image. The second image may bereconstructed by performing a plurality of iterations based on anobjective function including the statistical model of noises obtained in604. As still an example, the first projection data may be denoisedbased on the statistical model of noises in a projection domain obtainedin 604. The second image may be generated based on the denoised firstprojection data. The second image may be denoised based on thestatistical model of noises in an image domain. More descriptions ofdetermining the second image data based on the statistical model ofnoises may be found in FIGS. 9, 10, and/or 11 and the descriptionsthereof.

In 610, the ROI of the subject may be determined based on the secondimage data. Operation 610 may be performed by the image data image dataprocessing module 406. In some embodiments, the determination of the ROIof the subject may include identifying the ROI or tissues around theROI, drawing the outline of the ROI, obtaining information relating tothe ROI, etc. In some embodiments, a treatment plan of the subject maybe determined based on the determined information relating to the ROI ofthe subject. In some embodiments, the information relating to the ROImay be recorded for further processing. For example, the informationrelating to the ROI determined based on the second image data may becompared with information relating to the ROI acquired from apredetermined treatment plan of the subject. Then, a position of thefirst subject in space may be adjusted based on the comparison, forexample, via moving or rotating a couch of an IGRT apparatus (e.g., thecouch 116), such that radiation of a certain dose may be delivered tothe subject or the ROI of the subject according to a predeterminedtreatment plan of the subject. In some embodiments, the predeterminedtreatment plan of the subject may be modified based on the comparison.In some embodiments, when during or after a treatment, a delivery oftreatment radiation beam may be performed based on the comparison. Forexample, if the comparison is outside a range, a delivery of treatmentradiation beam may be deactivated. If the comparison is within a range,a delivery of treatment radiation beam may be reactivated.

In some embodiments, the predetermined treatment plan of the firstsubject may be obtained from the storage 130, the terminal(s) 140, thestorage module 408, or any other external storage. In some embodiments,the predetermined treatment plan of the subject may be determined by,for example, the diagnostic and treatment system 100 or other systems(e.g., a treatment planning system (TPS) connected to the diagnostic andtreatment system 100).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,operation 606 may be omitted. As another example, in 608, the secondimage data may be determined based on the preprocessed first image datadetermined in 606. In some embodiments, process 600 may further includedetermining or adjusting a treatment plan based on the determined ROI in610. In some embodiments, process 600 may further include adjust aposition of the subject based on the determined ROI in 610.

FIG. 7 is a flowchart illustrating an exemplary process 700 fordetermining a neural network model for denoising according to someembodiments of the present disclosure. In some embodiments, one or moreoperations of process 700 illustrated in FIG. 7 may be implemented inthe diagnostic and treatment system 100 illustrated in FIG. 1. Forexample, the process 700 illustrated in FIG. 7 may be stored in thestorage 130 in the form of instructions, and invoked and/or executed bythe processing device 120 (e.g., the processor 210 of the computingdevice 200 as illustrated in FIG. 2, the GPU 330 or CPU 340 of themobile device 300 as illustrated in FIG. 3). The model for denoisingmentioned in operation 604 may be determined according to process 700.

In 702, multiple groups of training data relating to multiple firstsubjects may be obtained, each of the multiple groups of training dataincluding low-dose image data and high-dose image data relating to afirst subject. Operation 702 may be performed by the acquisition module402. The multiple groups of training data relating to multiple ROIs ofthe multiple first subjects may be obtained from the IGRT apparatus 110,the storage 130, the terminal(s) 140, the storage module 408, thestorage unit 508, and/or other external storages. The high-dose imagedata may include high-dose projection data or a high-dose imagecorresponding to a first equivalent dose level. The low-dose image datamay include low-dose projection data or low-dose image corresponding toa second equivalent dose level lower than the first equivalent doselevel. In some embodiments, the first equivalent dose level and thesecond equivalent dose level may vary according to clinical demands(e.g., a type of a tissue). For example, in a liver scan, the firstequivalent dose level may be equal to or exceed 5 mSv, or 10 mSv, or 15mSv, etc. The second equivalent dose level may be lower than 15 mSv, or10 mSv, or 5 mSv, etc. A ratio of the second equivalent dose level tothe first equivalent dose level may range from 5% to 40%, such as 10%,15%, 20%, 25%, 30%, etc. As another example, in a chest scan, the firstequivalent dose level may be equal to or exceed 2 mSv, or 7 mSv, etc.The second equivalent dose level may be lower than 7 mSv, or 2 mSv, etc.In some embodiments, a ratio of the first equivalent dose level to anestimated effective dose may be equal to or exceed 1%, or 5%, or 10%, or25%, or 50%, or 100%, or 150%, etc. A ratio of the second equivalentdose level to the estimated effective dose may be equal to or below 1%,or 5%, or 10%, or 25%, etc.

In some embodiments, both the high-dose image data and the correspondinglow-dose image data may be obtained from an imaging device (e.g., theimaging device 112 of the IGRT apparatus 110) generated by scanning oneof the multiple first subjects being examined. As used herein, thecorresponding high-dose image data and the low-dose image data may referto a representation of a same portion of the first subject (e.g., an ROIof the second subject). In some embodiments, the low-dose image datacorresponding to the high-dose image data may be determined based on thehigh-dose image data. For example, the low-dose image data may includelow-dose projection data. The high-dose image data may include high-doseprojection data. The low-dose projection data may be determined by wayof simulation based on the high-dose projection data. More descriptionsof determining the low-dose image data based on the correspondinghigh-dose image data may be found in, for example, InternationalApplication No PCT/CN2017/095071, entitled “SYSTEM AND METHOD FOR IMAGECONVERSION,” filed Jul. 28, 2017, the contents of which are herebyincorporated by reference.

In 704, a neural network model may be trained using the multiple groupsof training data to obtain a general neural network model for denoising.Operation 704 may be performed by the neural network model determinationunit 502. In some embodiments, the neural network model may bepre-determined (e.g., provided by a manufacturer of an IGRT apparatus,an entity specializing in image processing, an entity having access totraining data, etc.). In some embodiments, the neural network model maybe obtained from the storage 130, the storage module 408, the storageunit 508, the terminal(s) 140, or other storages. In some embodiments,the neural network model may be constructed based on a back propagation(BP) neural network, a convolutional neural network (CNN), a recurrentneural network (RNN), a long short-term memory (LS™), a generativeadversarial network (GAN), an adaptive resonance theory (ART) neuralnetwork, or the like, or a combination thereof. In some embodiments, theneural network model may be constructed as a two-dimensional (2D) model,a three-dimensional (3D) model, a four-dimensional (4D) model, or amodel of any other dimensions. See, for example, FIG. 12 and thedescription thereof. In some embodiments, the neural network model maybe trained by inputting each group of the multiple groups of trainingdata using a machine training algorithm. Exemplary machine trainingalgorithms may include a gradient descent algorithm, a Newton algorithm,a conjugate gradient algorithm, a Quasi-Newton algorithm, aLevenberg-Marguardt algorithm, or the like, or a combination thereof.More descriptions of the general neural network model for denoising maybe found in, for example, International Application No.PCT/CN2017/095071, entitled “SYSTEM AND METHOD FOR IMAGE CONVERSION,”filed Jul. 28, 2017, the contents of which are hereby incorporated byreference.

In some embodiments, multiple pairs of image blocks may be extractedfrom each group of the training data relating to multiple firstsubjects. As used herein, each pair of the multiple pairs of imageblocks may include an image block extracted from the high-dose imagedata and an image block extracted from the low-dose image data relatingto one of the multiple first subjects. A pair of image blocks maycorrespond to the same region in the ROI of one of the multiple firstsubjects. In some embodiments, the multiple first subjects may include aspecific subject (e.g., the second subject as described in 706). Themultiple groups of training data relating to the multiple first subjectsmay include a group of training data relating to the specific subject(e.g., the second subject as described in 706). Further, multiple pairsof image blocks relating to the specific subject (e.g., the secondsubject described in 706) may be extracted from the group of trainingdata relating to the specific subject (e.g., the second subject asdescribed in 706). The neural network model may be trained iteratively.In each iteration, at least one portion of multiple pairs of imageblocks relating to multiple second subjects including multiple pairs ofimage blocks relating to the specified subject (e.g., the second subjectdescribed in 706) may be selected based on, for example, a stochasticgradient descent algorithm, to train the neural network model.Parameters (e.g., weight value) of the neural network may be adjusted ineach iteration until, for example, all the multiple pairs of imageblocks may be used to train the neural network model, or a certainnumber of iterations are performed.

In 706, training data of a second subject including low-dose image dataand high-dose image data relating to an ROI of the second subject may beobtained. Operation 706 may be performed by the acquisition module 402.In some embodiments, the training data of the second subject may includehigh-dose image data and corresponding low-dose image data. As usedherein, the corresponding high-dose image data and the low-dose imagedata may refer to a representation of a same portion of the secondsubject (e.g., the ROI of the second subject). In some embodiments, thehigh-dose image data may be acquired by a first device. The low-doseimage data may be acquired by a second device. The first device may besame as or different from the second device. For example, the firstdevice may include an imaging device, such as a CT device. The seconddevice may include an imaging device of an IGRT apparatus (e.g., theimaging device 112 of the IGRT apparatus 110). In some embodiments, themultiple first subjects may include the second subject. Further, themultiple groups of the training data relating to the multiple firstsubjects may include the training data of the second subject.

In some embodiments, the high-dose image data may be used to determine atreatment plan of the second subject. For example, the high-dose imagedata may include planning image data (e.g., a planning image). In someembodiments, the high-dose image data may be used to guide theimplementation of a predetermined treatment plan of the second subject.For example, the high-dose image data may include guiding image data(e.g., a guiding image) relating to the ROI of the second subject asdescribed in connection with FIG. 6. In some embodiments, the high-doseimage data may include fused image data (e.g., a fused image) relatingto the ROI of the subject. For example, the fused image relating to theROI of the second subject may be generated by fusing at least two of theguiding images relating to the ROI of the second subject acquired duringa treatment period of the second subject. As another example, the fusedimage relating to the ROI of the second subject may be generated byfusing the planning image and one of the guiding images. In someembodiments, the high-dose image data may include a registration imagerelating to the ROI of the second subject. For example, the high-doseimage data may include a CT-MRI registration image, a CT-PETregistration image, etc.

The high-dose image data may correspond to a third equivalent doselevel. The low-dose image data may correspond to a fourth equivalentdose level. The third equivalent dose level may be higher than thefourth equivalent dose level. In some embodiments, the third equivalentdose level and/or the fourth equivalent dose level may vary according toclinical demands (e.g., the type of a tissue). For example, in a liverscan, the third equivalent dose level may be equal to or exceed 5 mSv,or 10 mSv, or 15 mSv, etc. The fourth equivalent dose level may be lowerthan 15 mSv, or 10 mSv, or 5 mSv, etc. As another example, in a chestscan, the third equivalent dose level may be equal to or exceed 2 mSv,or 7 mSv, etc. The fourth equivalent dose level may be lower than 7 mSv,or 2 mSv, etc. As still an example, in an epigastrium scan with a CBCTdevice, the fourth equivalent dose level may be equal to 4 mGy. In anepigastrium scan with a CT device under scanning parameters 120 kv and30 mAs, the fourth equivalent dose level may be equal to 1.5 mGy. In anepigastrium scan with a CT device under scanning parameters 120 kv and220 mAs, the third equivalent dose level may be equal to 14 mGy.

The third equivalent dose level and the fourth equivalent dose level mayvary according to clinical demands. For example, in a liver scan, aratio of the fourth equivalent dose level to the third equivalent doselevel may range from 5% to 40%, such as 10%, 15%, 20%, 25%, 30%, 50%,85%, etc. In an epigastrium scan, a ratio of the fourth equivalent doselevel to the third equivalent dose level may range from equal to orexceed 10%, or 15%, or 25%, or 30%, or 40%, or 50%, 85%, etc. Forexample, for a CBCT scan, the ratio of the fourth equivalent dose levelto the third equivalent dose level may equals to or exceeds 1:3, or 1:2.For a CT scan, the ratio of the fourth equivalent dose level to thethird equivalent dose level may equal to 1:7, or 1:5.

In 708, the general neural network model for denoising may be trainedusing the training data of the second subject to obtain a personalizedneural network model for denoising. Operation 708 may be performed bythe neural network model determination unit 502.

In some embodiments, multiple pairs of image blocks may be extractedfrom the high-dose image and the low-dose image relating to the secondsubject as the training data of the general neural network model fordenoising. As used herein, each pair of the multiple pairs of imageblocks may include a first image block extracted from the high-doseimage and a second image block extracted from the low-dose image. Thefirst image block and the second image block may correspond to the sameregion in the ROI of the second subject. In some embodiments, the firstimage block and/or the second image block may be extracted based on arandom sampling algorithm. Exemplary random sampling algorithms mayinclude an acceptance-rejection sampling algorithm, an importancesampling algorithm, a Metropolis-Hasting algorithm, a Gibbs samplingalgorithm, etc. In some embodiments, the first image block and/or thesecond image block may be extracted based on an instruction provided bya user via the terminal(s) 140.

The general network model for denoising may be trained based on themultiple pairs of image blocks using a machine training algorithm asdescribed above. In some embodiments, a cost function (e.g., a lostfunction) may be used to training the general neural network model fordenoising. The cost function may be configured to assess a differencebetween a testing value (e.g., the second image block of the low-doseimage) and a desired value (e.g., the first image block of the high-doseimage). In some embodiments, the second image block of the low-doseimage may be inputted to the general neural network model for denoisingvia an input layer (e.g., the input layer 1120 as illustrated in FIG.12). The second image block of the low-dose image may be transferredfrom a first hidden layer of the neural network model (e.g., theconventional layers 1140-1 as illustrated in FIG. 12) to the last hiddenlayer of the general neural network model for denoising. The secondimage block of the low-dose image may be processed in each of themultiple hidden layers via performing an image transformation operation,an image enhancement operation, an image denoising operation, or anyother operations. The processed second image block of the low-dose imageprocessed by the hidden layers may be inputted to the cost functionlayer. The value of the cost function may be determined based on theprocessed the second image block of the low-dose image and the firstimage block of the high-dose image. Then, a parameter (e.g., a weighvalue) of the general neural network model for denoising may be adjustedin the training process until the value of the cost function satisfies acondition (e.g., a predetermined threshold).

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,operations 702 and 706 may be performed simultaneously or in a reverseorder than that illustrated in FIG. 7, and/or operations 704 and 706 maybe performed simultaneously or in a reverse order than that illustratedin FIG. 7. As another example, operations 702 and 704 may be omitted.The personalized neural network model for denoising may be determined bytraining a neural network model based on the training data relating tothe second subject. In some embodiments, process 700 may further includestoring the training data of the second subject and/or the multiplegroups of training data relating to multiple first subjects in thestorage 130, the terminals 140, the storage module 450, the storage unit580, and/or other external storage devices.

FIG. 8 is a flowchart illustrating an exemplary process 800 forprocessing low-dose image based on a neural network model for denoisingaccording to some embodiments of the present disclosure. In someembodiments, one or more operations of process 800 illustrated in FIG. 8may be implemented in the diagnostic and treatment system 100illustrated in FIG. 1. For example, the process 800 illustrated in FIG.8 may be stored in the storage 130 in the form of instructions, andinvoked and/or executed by the processing device 120 (e.g., theprocessor 210 of the computing device 200 as illustrated in FIG. 2, theGPU 330 or CPU 340 of the mobile device 300 as illustrated in FIG. 3).Operation 608 may be performed according to process 800.

In 802, a low-dose image relating to an ROI of a subject may beprocessed using the neural network model for denoising to determinenoise data included in the low-dose image data. Operation 802 may beperformed by the imaging data denoising unit 504. The low-dose image(e.g., the first image data obtained in 602) relating to the ROI of thesubject may correspond to a low-dose level (e.g., the first equivalentdose level as descried in 602). The low-dose image may exhibit a qualityof image lower than that of high-dose image (e.g., the second image dataas described in 608) corresponding to the low-dose image. As usedherein, the quality of image may be defined by a noise level of theimage. The noise level of the low-dose image may be higher than that ofhigh-dose image corresponding to the low-dose image data. The neuralnetwork model for denoising may be used to extract the noise data fromthe low-dose image. The noise data extracted from the low-dose imageusing the neural network model for denoising may include a noise imagerepresenting noises included in the low-dose image.

The neural network model for denoising may be obtained from the storage130, the storage module 408, the storage unit 508, or other externalstorage. In some embodiments, the neural network model for denoising maybe pre-determined (e.g., provided by a manufacturer of an IGRTapparatus, an entity specializing in image processing, an entity havingaccess to training data, etc.). In some embodiments, the neural networkmodel for denoising may be determined according to process 700 asillustrated in FIG. 7. In some embodiments, the neural network model fordenoising may include a general neural network model for denoising or apersonalized neural network model for denoising corresponding to thesubject.

In 804, a high-dose image relating to the ROI of the subject may bedetermined based in the noise data and the low-dose image. Operation 804may be performed by the imaging data denoising unit 504. The high-doseimage (e.g., the second image data obtained in 608) may be determined bya combination of noise data and the low-dose image (e.g., the firstimage data obtained in 602). Further, the high-dose image (e.g., thesecond image data obtained in 608) may be determined by performing asubtraction operation between the noise data (e.g., the noise image) andthe low-dose image. For example, the noise data (e.g., the noise image)may include a plurality of first pixels or voxels. The low-dose image(e.g., the first image data obtained in 602) may include a plurality ofsecond pixels or voxels. The high-dose image (e.g., the second imagedata obtained in 608) may include a plurality of third pixels or voxels.A gray value of a third pixel or voxel in the high-dose image may beequal to a subtraction between a gray value of a corresponding pixel orvoxel in the low-dose image and a gray value of a corresponding pixel orvoxel in the noise image. As used herein, the corresponding pixel orvoxel in the low-dose image, the high-dose image, and the noise imagemay refer to three pixels or voxels that represent the same spatialpoint or region of the ROI of the subject.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,operation 802 may be omitted. The low-dose image may be converted to thehigh-dose image directly using the neural network model for denoising.In some embodiments, the neural network model for denoising may beconfigured to extract the noise data from low-dose projection data. Thenoise data may be converted to a noise image. The low-dose projectiondata may be processed to obtain a low-dose image. The high-dose imagemay be determined based on the reconstructed low-dose image and thenoise image.

FIG. 9 is a flowchart illustrating an exemplary process 900 forprocessing low-dose image data based on a statistical model of noisesaccording to some embodiments of the present disclosure. In someembodiments, one or more operations of process 900 illustrated in FIG. 9may be implemented in the diagnostic and treatment system 100illustrated in FIG. 1. For example, the process 900 illustrated in FIG.9 may be stored in the storage 130 in the form of instructions, andinvoked and/or executed by the processing device 120 (e.g., theprocessor 210 of the computing device 200 as illustrated in FIG. 2, theGPU 330 or CPU 340 of the mobile device 300 as illustrated in FIG. 3).Operation 608 may be performed according to process 900.

In 902, first projection data may be processed based on a noiseestimation in a projection domain to obtain second projection data.Operation 902 may be performed by the iterative reconstruction unit 506.The first projection data may include low-dose projection datacorresponding to a first equivalent dose level (e.g., the firstequivalent dose level of the first image data obtained in 602). Thelow-dose projection data (the first image data obtained in 602) may beobtained from the IGRT apparatus 110, the storage module 408, thestorage unit 508, or other storages as described in connection withoperation 602. The noise estimation may be obtained from the storage130, the storage module 408, the storage unit 508, or other storages asdescribed in connection with 604. The noise estimation may represent anoise level of the first projection data. The noise estimation may bedetermined based on the first projection data and/or according a defaultsetting of the diagnostic and treatment system 100 as describedelsewhere in the disclosure. See, for example, FIG. 6 and thedescription thereof.

In some embodiments, the first projection data may be denoised based onthe noise estimation using a nonlinear filtering algorithm. Exemplarynonlinear filtering algorithms may include an extended Calman filter(EKF) algorithm, an unscented filter Calman (UFK) algorithm, a particlefilter (PF) algorithm, etc. For example, the nonlinear filteringalgorithm may be performed based on an objective function represented byEquation (1) as below:

minimize{∫|∇n|+β*(δ_(n)(γ,ξ)^(−b)*∫(n(γ,ξ)−n₀(γ,ξ))²dx},  (1),

where, n(γ,ξ) denotes the second projection data, n₀(γ,ξ) denotes thefirst projection data, ∇n denotes a total variation, (δ_(n)(γ,ξ))^(−b)denotes the noise estimation relating to the first projection data, βdenotes a parameter configured to adjust a strength of denoising, γdenotes a channel count of a detector in an imaging device (e.g., theimaging device 112 of the IGRT apparatus 110), ξ denotes a row count ofthe detector in the imaging device (e.g., the imaging device 112 of theIGRT apparatus 110), and b denotes a constant. For instance, b may be aconstant in the range from 0 to 5, such as 0, 0.5, 1, 1.5, 2, 2.5, 3,3.5, 4, 4.5, or 5. As illustrated in Equation (1), the second projectiondata may be determined by performing a plurality of iterations based onthe objective function (i.e., Equation (1)) until a condition issatisfied. An exemplary condition is that the value of the objectivefunction (i.e., Equation (1)) is at least locally minimum when thecondition is satisfied. Merely by way of example, the condition is thatthe value of the objective function (i.e., Equation (1)) is globallyminimum when the condition is satisfied. Another exemplary condition isthat a specified number of iterations are performed. A further exemplarycondition is that the change of the value of the objective function intwo or more consecutive iterations may be equal to or smaller than athreshold. The strength of denoising for the first projection data maybe controlled by the noise estimation and the value of β. The greaterthe product between (δ_(n)(γ,ξ)^(−b) and β is, the smaller the strengthof denoising for the first projection data may be. The greater the noiselevel of the first projection data is, the smaller the value of(δ_(n)(γ,ξ))^(−b) may be, the smaller the product between(δ_(n)(γ,ξ))^(−b) and β may be, and the greater the strength ofdenoising for the first projection data may be.

In 904, a first image may be generated based on the second projectiondata. Operation 904 may be performed based on the iterativereconstruction unit 506. In some embodiments, the first image may begenerated based on the second projection data using an imagereconstruction algorithm. Exemplary image reconstruction algorithms mayinclude a filtered back projection (FBP) algorithm, an algebraicreconstruction technique (ART), a local reconstruction algorithm, or thelike, or a combination thereof.

In 906, a statistical model of noises in an image domain may bedetermined based on the noise estimation in the projection domain.Operation 906 may be performed based on the iterative reconstructionunit 506. In some embodiments, the statistical model of noises in theimage domain may be determined by performing a back projection operationon the noise estimation in the projection domain. Further, the noiseestimation in the projection domain may be also referred to as noiseprojection data. The statistical model of noises in the image domain maybe also referred to as a noise image. The noise image may be generatedbased on the noise projection data using a filtered back projection(FBP) algorithm. More descriptions of the statistical model of noises inthe image domain and/or the noise estimation in the projection domainmay be found in, for example, Chinese Publication No 1039713876,entitled “SYSTEM AND METHOD FOR CT IMAGE RECONSTRUCTION,” the contentsof which are hereby incorporated by reference.

In 908, a second image may be determined based on the first image andthe statistical model of noises in the image domain. Operation 908 maybe performed based on the iterative reconstruction unit 506. In someembodiments, the first image may be denoised based on the statisticalmodel of noises in the image domain using a nonlinear filteringalgorithm as described above. Further, the nonlinear filtering algorithmmay be performed based on an objective function represented by Equation(2) as below:

minimize{∫|∇u|dxdy+β*(δ_(u)(x,y))^(−b)*∫(u(x,y)−u₀(x,y))²dxdy}  (2),

where u₀(x,y) denotes a gray value of a pixel value in the first image,u(x,y) denotes a gray value of a pixel in the second image, ∇u denotes atotal variation of the second image, (δ_(u)(x,y))^(−b) denotes thestatistical model of noises in the image domain, β denotes a parameterrelating the statistical model of noises in the image domain configuredto adjust a strength of denoising, b denotes a constant, such as −2,−2.5, or −3. As illustrated in Equation (2), the second image may bedetermined by performing a plurality of iterations based on theobjective function (i.e., Equation (2)) until a condition is satisfied.An exemplary condition is that the value of the objective function(i.e., Equation (2)) is at least locally minimum when the condition issatisfied. Merely by way of example, the condition is that the value ofthe objective function (i.e., Equation (1)) is globally minimum when thecondition is satisfied. Another exemplary condition is that a specifiednumber of iterations are performed. A further exemplary condition isthat the change of the value of the objective function in two or moreconsecutive iterations may be equal to or smaller than a threshold. Thestrength of denoising for the first image may be controlled by thestatistical model of noises in the image domain and the value of β. Thegreater the product between (δ_(u)(x,y))^(−b) and β is, the smaller thestrength of denoising for the first image may be. The greater the noiselevel of the first image is, the smaller the value of (δ_(u)(x,y))^(−b)may be, the smaller the product between (δ_(u)(x,y))^(−b) and β may be,and the greater the strength of denoising for the first image may be.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, thelow-dose image data and the high-dose image data may include low-doseprojection data and the high-dose projection data, respectively. Forexample, process 900 may include pre-processing the first projectiondata. As another example, operations 904 and 906 may be performedsimultaneously or in a reverse order than that illustrated in FIG. 9.

FIG. 10 is a flowchart illustrating an exemplary process 1000 forprocessing low-dose image data based on an iterative reconstructiontechnique according to some embodiments of the present disclosure. Insome embodiments, one or more operations of process 1000 illustrated inFIG. 10 may be implemented in the diagnostic and treatment system 100illustrated in FIG. 1. For example, the process 900 illustrated in FIG.10 may be stored in the storage 130 in the form of instructions, andinvoked and/or executed by the processing device 120 (e.g., theprocessor 210 of the computing device 200 as illustrated in FIG. 2, theGPU 330 or CPU 340 of the mobile device 300 as illustrated in FIG. 3).Operation 608 may be performed according to process 1000.

In 1002, low-dose projection data may be processed using a statisticalmodel of noises. Operation 1002 may be performed by the iterativereconstruction unit 504. The statistical model of noises may be obtainedas described in connection with operation 604.

In 1004, a regularization item for denoising may be determined.Operation 1004 may be performed by the iterative reconstruction unit506. As used herein, the regularization item (e.g., βR(X) in Equation(3)) may refer to an item that may be used to regularize imageestimate(s) generated during an iterative reconstruction process. Insome embodiments, the regularization item may be defined by aregularization parameter and a regularization function. For example, theregularization item may be determined by multiplying the regularizationparameter and the regularization function. In some embodiments, theregularization item may relate to a denoising model (e.g., thestatistical model of noises). For example, the regularization parametermay control the strength of the regularization item (also referred to asthe intensity for denoising) based on the denoising model (e.g., thestatistical model of noises). In some embodiments, the regularizationitem may be determined based on a sparsity of low-dose projection data.In some embodiments, the low-dose projection data may be represented bya matrix including a plurality of elements. The sparsity of the low-doseprojection data may refer to a ratio of the number of zero-valuedelements to the total number of the plurality of elements in the matrixof the low-dose projection data. Further, the regularization parametermay be determined based on the sparsity of low-dose projection data. Thegreater the sparsity of low-dose projection data is, the greater theregularization parameter may be.

The regularization parameter may be used to control the strength of theregularization item (also referred to as the intensity for denoising).In some embodiments, the regularization parameter may include a set ofelements. The regularization parameter may be expressed in the form of amatrix. Each of the set of elements may correspond to an element in animage estimate. For example, if an image estimate has 8×9 pixels, theregularization parameter may include 72 elements. Each of the 72elements may correspond to a pixel of the image estimate. In someembodiments, the regularization parameter may be determined based on thestatistical model of noises in the image domain. For example, thegreater the element in the statistical model of noises is, the greaterthe corresponding element in the regularization parameter may be. Asused herein, an element in the statistical model of noises and acorresponding element in the regularization parameter may refer to twoelements corresponding to a same pixel in an image estimate.

In 1006, an objective function may be determined based on theregularization item and the statistical model of noises. Operation 1006may be performed by the iterative reconstruction unit 506. In someembodiments, the objective function may be determined based on a leastsquares technique. The least squares technique may be used to determinean optimal solution that at least locally minimizes the sum of thesquares of the difference between a value estimate and an observedvalue. As used herein, an optimal solution may refer to a target image,the value estimate may refer to a projection estimate corresponding toan image estimate generated in an iteration, and the observed value mayrefer to the low-dose projection data. As used herein, the sum of thesquares of the difference between a value estimate and an observed valuemay be considered locally minimum when, for example, a value of theobjective function is smaller than a constant, a specified number ofiterations are performed, the objective function converges, etc.

For illustration purposes, the objective function may be expressed bythe following Equation (3):

$\begin{matrix}{{{f(X)} = {{\min\limits_{X \geq 0}{{{AX} - Y}}_{w}^{2}} + {\beta \; {R(X)}}}},} & (3)\end{matrix}$

where f(X) denotes the objective function, X denotes an image to bereconstructed (also referred to as an image estimate, or a targetimage), Y denotes the processed low-dose projection data, A denotes aprojection matrix, βR(X) denotes a regularization item, β denotes aregularization parameter (also referred to as a penalty coefficient),R(X) denotes a regularization function, w denotes a statistical weight(e.g., a constant) determined based on the statistical model of noisesas described elsewhere in the disclosure, and

$\min\limits_{X \geq 0}$

denotes a preset condition or a constraint.

In 1008, a target image may be generated based on the objective functionand the low-dose projection data by performing a plurality ofiterations. Operation 1008 may be performed by the iterativereconstruction unit 506. The objective function may be used to determinethe target image (also referred to as an optimal image) by globallyminimizing a value of the objective function. In some embodiments, theplurality of iterations may be performed according to process 1100 asdescribed in connection with FIG. 11. In some embodiments, an imageestimate may be determined in an iteration. A projection estimatecorresponding to the image estimate may be determined by projecting theimage estimate onto a specific projection plane. The projection estimatemay be compared with the processed low-dose projection data, and atarget image may be determined by updating the image estimate based on adifference between the projection estimate and the processed low-doseprojection data.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,process 1000 may include pre-processing the low-dose projection data. Asanother example, operations 1004 and 1006 may be performedsimultaneously or in a reverse order than that illustrated in FIG. 10.

FIG. 11 is a flowchart illustrating an exemplary process 1100 forprocessing low-dose image data based on an iterative reconstructiontechnique according to some embodiments of the present disclosure. Insome embodiments, one or more operations of process 1100 illustrated inFIG. 11 may be implemented in the diagnostic and treatment system 100illustrated in FIG. 1. For example, the process 1100 illustrated in FIG.11 may be stored in the storage 130 in the form of instructions, andinvoked and/or executed by the processing device 120 (e.g., theprocessor 210 of the computing device 200 as illustrated in FIG. 2, theGPU 330 or CPU 340 of the mobile device 300 as illustrated in FIG. 3).Operation 1008 may be performed according to process 1100.

In 1102, a statistical model of noises corresponding to first projectiondata relating to an ROI of a subject may be obtained. Operation 1102 maybe performed by the acquisition module 402. The statistical model ofnoises may be obtained from the storage 130, the storage module 408, thestorage unit 508, the terminal(s) 140, or other storages. Thestatistical model of noises may be determined as described elsewhere inthe present disclosure. See, for example, FIGS. 6, 9, and 10 and thedescriptions thereof. The first projection data may include theprocessed low-dose projection data determined as described in connectionwith FIG. 10.

In 1104, an initialized image may be obtained. Operation 1104 may beperformed by the iterative reconstruction unit 506. In some embodiments,the initialized image may include a plurality of pixels or voxels withestimated characteristics, such as gray value, intensity, color, etc. Insome embodiments, the initialized image may be predetermined by a uservia the terminal(s) 140 or according to a default setting of thediagnostic and treatment system 100. In some embodiments, the grayvalues of pixels or voxels in the initialized image may be set asdifferent values or the same value. For example, the gray values ofpixels or voxels in the initial image estimate may be set as 0. In someembodiments, the initialized image may be determined by performing afiltered back projection (FBP) operation on the first projection data.

In 1106, second projection data corresponding to the initialized imagemay be determined. Operation 1106 may be performed by the iterativereconstruction unit 506. The second projection data corresponding to theinitialized image may be determined by projecting the initialized imageonto a specific projection plane. In some embodiments, the secondprojection data may be determined based on the initialized image and aprojection matrix. For example, the second projection data may bedetermined by multiplying the projection matrix by the initializedimage. In some embodiments, the projection matrix may be predeterminedaccording to a default setting of the diagnostic and treatment system100, or may be adjusted by a user (e.g., a doctor).

In 1108, third projection data indicating a difference between the firstprojection data and the second projection data may be determined.Operation 1108 may be performed by the iterative reconstruction unit506. In some embodiments, the third projection data may be determinedbased on a subtraction of the first projection data and the secondprojection data.

In 1110, the third projection data may be processed based on thestatistical model of noises to obtain processed third projection data.Operation 1010 may be performed by the iterative reconstruction unit506. In some embodiments, the third projection data may include aplurality of subsets of data. The plurality of subsets of data may berepresented by a first matrix. The statistical model of noises may berepresented by a second matrix. The second matrix may include aweighting matrix including a plurality of weighting factors in a rangefrom 0 to 1. The processed third projection data may be determined byperforming a dot product of the first matrix and the second matrix. Asused herein, the dot product of the first matrix and the second matrixmay be determined by multiplying a total amount of data in each of theplurality of the subsets of data and the corresponding weighting factorin the second matrix. For example, if the weighting factor is 1, thewhole subset of data may be included in the processed third projectiondata. As another example, if the weighting factor is 0.5, the subset ofdata multiplied by 0.5 may be included in the processed third projectiondata. As still another example, if the weighting factor is 0, the wholesubset of data may be excluded from the processed third projection data.

In 1112, an error image is determined based on the processed thirdprojection data. Operation 1112 may be performed by the iterativereconstruction unit 506. The error image may be generated by performinga back projection operation on the processed third projection data.

In 1114, the initialized image may be updated. Operation 1114 may beperformed by the iterative reconstruction unit 506. In some embodiments,a plurality of iterations may be performed based on the objectivefunction. While in an iteration other than the first iteration, theinitialized image may be updated based on a reconstructed image (e.g.,an image estimate) generated in a previous iteration based on the firstprojection data.

In 1116, a determination may be made as to whether a condition issatisfied. Operation 1116 may be performed by the iterativereconstruction unit 506. If the condition is satisfied, process 1100 mayproceed to operation 1118. If the condition is not satisfied, process1100 may proceed to operation 1106. In some embodiments, the conditionmay be assessed based on a value of the objective function or the errorimage generated in an iteration. For example, the condition may includethat the value of the objective function may be minimal or smaller thana threshold, the change of the value of the objective function in two ormore consecutive iterations may be equal to or smaller than a threshold,the difference between the value of the objective function and a targetvalue is equal to or smaller than a threshold, etc. As another example,the condition may include that the change of the average gray value ofpixels or voxels in the error image generated in two or more consecutiveiterations may be equal to or smaller than a threshold, such as 0, orthe difference between the average gray value of pixels or voxels in theerror image and a target value is below a threshold. In someembodiments, the condition may be satisfied when a specified number ofiterations are performed.

In 1118, the initialized image may be designated as a target imagerelating to the subject. Operation 1118 may be performed by theiterative reconstruction unit 506. The target image may correspond to anoptimal solution of the objective function. The target image maycorrespond to a dose level higher than that of the first projectiondata.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,process 1100 may include pre-processing the first projection data. Asanother example, operations 1104 and 1106 may be performedsimultaneously or in a reverse order than that illustrated in FIG. 11.It may be indicated that the iteration process 1100 may terminate afterthe condition is satisfied.

FIG. 12 is a schematic diagram illustrating an exemplary convolutionalneural network (CNN) model according to some embodiments of the presentdisclosure.

The CNN model may include an input layer 1220, multiple hidden layers1240, and an output layer 1260. The multiple hidden layers 1240 mayinclude one or more convolutional layers, one or more Rectified LinearUnits layers (ReLU layers), one or more pooling layers, one or morefully connected layers, or the like, or a combination thereof.

For illustration purposes, exemplary hidden layers 1240 of the CNNmodel, including a convolutional layer 1240-1, a pooling layer 1240-2,and a fully connected layer 1240-N, are illustrated. As described inconnection with process 700, the model determination unit 502 mayacquire low-dose image as an input of the CNN model. The low-dose imagemay be expressed as a two-dimensional (2D) or three-dimensional (3D)matrix including a plurality of elements (e.g., pixels or voxels). Eachof the plurality of elements in the matrix may have a value (alsoreferred to as pixel/voxel value) representing a characteristic of theelement.

The convolutional layer 1240-1 may include a plurality of kernels (e.g.,A, B, C, and D). The plurality of kernels may be used to extractfeatures of the low-dose image data. In some embodiments, each of theplurality of kernels may filter a portion (e.g., a region) of thelow-dose image to produce a specific feature corresponding to theportion of the low-dose image data. The feature may include a low-levelfeature (e.g., an edge feature, a texture feature), a high-levelfeature, or a complicated feature that is calculated based on thekernel(s).

The pooling layer 1240-2 may take the output of the convolutional layer1240-1 as an input. The pooling layer 1240-2 may include a plurality ofpooling nodes (e.g., E, F, G, and H). The plurality of pooling nodes maybe used to sample the output of the convolutional layer 1240-1, and thusmay reduce the computational load of data processing and increase thespeed of data processing of the diagnostic and treatment system 100. Insome embodiments, neural network model determination unit 502 may reducethe volume of the matrix corresponding to the low-dose image in thepooling layer 1240-2.

The fully connected layer 1240-N may include a plurality of neurons(e.g., O, P, M, and N). The plurality of neurons may be connected to aplurality of nodes from the previous layer, such as a pooling layer. Inthe fully connected layer 1240-N, a plurality of vectors correspondingto the plurality of neurons may be determined based on the features ofthe low-dose image and further weigh the plurality of vectors with aplurality of weighting coefficients.

In the output layer 1260, an output, such a noise data (e.g., a noiseimage) may be determined based on the plurality of vectors and weightingcoefficients obtained in the fully connected layer 1240-N.

It shall be noted that the CNN model may be modified when applied indifferent conditions. For example, in a training process, a lossfunction (also referred to as a cost function) layer may be added tospecify the deviation between a predicted output (e.g., a predictednoise image) and a true label (e.g., reference high-dose imagecorresponding to the low-dose image).

In some embodiments, the neural network model determination unit 502 mayget access to multiple processing units, such as GPUs, in the diagnosticand treatment system 100. The multiple processing units may performparallel processing in some layers of the CNN model. The parallelprocessing may be performed in such a manner that the calculations ofdifferent nodes in a layer of the CNN model may be assigned to two ormore processing units. For example, one GPU may run the calculationscorresponding to kernels A and B, and the other GPU(s) may run thecalculations corresponding to kernels C and D in the convolutional layer1240-1. Similarly, the calculations corresponding to different nodes inother type of layers in the CNN model may be performed in parallel bythe multiple GPUs.

EXAMPLES

The examples are provided for illustration purposes, and not intended tolimit the scope of the present disclosure.

Example 1. Exemplary Images Corresponding to Different Dose Levels

FIG. 13A and FIG. 13B illustrate exemplary images corresponding todifferent dose levels according to some embodiments of the presentdisclosure. The first image shown in FIG. 13A and the second image shownin FIG. 13B represent the same abdomen of a subject. The first imagecorresponds to a first equivalent dose level. The second imagecorresponds to a second equivalent dose level lower than 85% of thefirst equivalent dose level. The noise level shown in the second imageis greater than that shown in the first image.

Example 2. Exemplary Images Corresponding to Different Dose Levels

FIG. 14A and FIG. 14B illustrate exemplary images corresponding todifferent dose levels according to some embodiments of the presentdisclosure. The first image shown in FIG. 14A, the second image shown inFIG. 14B, and the third image shown in FIG. 14C represent the same ROIof a subject. The first image corresponds to a first equivalent doselevel. The second image corresponds to a second equivalent dose levelhigher than the first equivalent dose level. The second image wasgenerated based on the first image using a neural network model fordenoising according to process 800. The third image corresponds to athird equivalent dose level higher than 85% of the first equivalent doselevel. The noise level shown in the second image is lower than thoseshown in the first image and the third image.

Example 3. Exemplary Images Corresponding to Different Dose LevelsGenerated Based on Different Reconstruction Algorithms

FIGS. 15A-15C illustrate exemplary images corresponding to differentdose levels generated based on different reconstruction algorithmsaccording to some embodiments of the present disclosure. The first imageshown in FIG. 15A, the second image shown in FIG. 15B, and the thirdimage shown in FIG. 15C represent the same ROI of a subject. The firstimage corresponds to a first equivalent dose level. The first image wasgenerated based on a FBP reconstruction algorithm. The second imagecorresponds to a second equivalent dose level. The second equivalentdose level was 55% of the first equivalent dose level. The second imagewas generated based on the FBP reconstruction algorithm. The third imagecorresponds to a third equivalent dose level same as the secondequivalent dose level. The third image was generated based on a Karlreconstruction algorithm according to process 900 as illustrated in FIG.9. The noise level shown in the third image is lower than those shown inthe first image and the second image.

Example 4. Exemplary Images Corresponding to the Same Dose LevelGenerated Based on Different Reconstruction Algorithms

FIGS. 16A and 16B illustrate exemplary images corresponding to the samedose level generated based on different reconstruction algorithmsaccording to some embodiments of the present disclosure. The first imageshown in FIG. 16A and the second image shown in FIG. 16B represent thesame ROI of a subject. The first image corresponds to a first equivalentdose level. The first image was generated based on a FBP reconstructionalgorithm. The second image corresponds to a second equivalent doselevel same as the first equivalent dose level. The second image wasgenerated based on the iterative reconstruction algorithm according toprocess 1000 and/or 1100. The noise level shown in the second image islower than that shown in the first image.

Example 5. Exemplary Images Corresponding to the Same Dose LevelGenerated Based on Different Reconstruction Algorithms

FIGS. 17A-17C illustrate exemplary images corresponding to the same doselevel generated based on different reconstruction algorithms accordingto some embodiments of the present disclosure. The first image shown inFIG. 17A, the second image shown in FIG. 17B, and the third image shownin FIG. 17C represent a body phantom of a head. The dose level of thefirst image, the second image, and the third image is same. The firstimage was generated based on a FBP reconstruction algorithm. The secondimage was generated based on according to process 900 as illustrated inFIG. 9. The third image was generated based on an iterativereconstruction algorithm according to process 1000 and/or 1100. Thenoise level shown in the third image is lower than that shown in thefirst image and the second image.

Example 6. Exemplary Images Corresponding to the Same Dose LevelGenerated Based on Different Reconstruction Algorithms

FIGS. 18A and 18B illustrate exemplary images corresponding to the samedose level generated based on different reconstruction algorithmsaccording to some embodiments of the present disclosure. The first imageshown in FIG. 18A and the second image shown in FIG. 18B represents thesame ROI of a subject. The dose level of the first image and the secondimage are same. The first image was generated according to process 900as illustrated in FIG. 9. The second image was generated according toprocess 1000 and/or 1100 as illustrated in FIG. 10 and/or FIG. 11. Thenoise level shown in the second image is lower than that shown in thefirst image.

Example 7. Exemplary Images Corresponding to the Same Dose LevelGenerated Based on Different Reconstruction Algorithms

FIGS. 19A and 19B illustrate exemplary images corresponding to the samedose level generated based on different reconstruction algorithmsaccording to some embodiments of the present disclosure. The first imageshown in FIG. 19A and the second image shown in FIG. 19B represented thesame ROI of a body. The dose level of the first image and the secondimage are same. The first image was generated according to process 1000as illustrated in FIG. 10. The second image was generated according toprocess 1000 and/or 1100 as illustrated in FIG. 10 and/or FIG. 11. Thesecond image was generated further based on sparsity of projection datacorresponding to the second image. The noise level shown in the secondimage is lower than that shown in the first image.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in a combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2103, Perl, COBOL2102, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations, therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution, for example, an installation on an existing server ormobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped in a single embodiment, figure, or description thereof for thepurpose of streamlining the disclosure aiding in the understanding ofone or more of the various inventive embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, inventive embodiments lie inless than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1-28. (canceled)
 29. A system, comprising a non-transitorycomputer-readable storage medium storing executable instructions, and atleast one processor in communication with the non-transitorycomputer-readable storage medium, when executing the executableinstructions, causing the system to implement a method, comprising:obtaining multiple groups of first training data, each of the multiplegroups of first training data including first image data correspondingto a first equivalent dose level and second image data corresponding toa second equivalent dose level higher than the first equivalent doselevel, the first image data and the second image data including arepresentation of a same portion of a subject; determining, based on themultiple groups of first training data, a general neural network model;obtaining one or more groups of second training data relating to atarget subject, each of the one or more groups of second training dataincluding third image data corresponding to a third equivalent doselevel and fourth image data corresponding to a fourth equivalent doselevel lower than the third equivalent dose level; and determining, basedon the multiple groups of second training data and the general neuralnetwork model for denoising, a personalized neural network model for thetarget subject.
 30. The system of claim 29, wherein the multiple groupsof first training data are related to multiple different subjects. 31.The system of claim 29, wherein the generating, based on the multiplegroups of first training data, a general neural network model fordenoising includes: training a neural network model using the multiplegroups of first training data to obtain the general neural networkmodel.
 32. The system of claim 29, wherein the determining, based on theone or more groups of second training data, a personalized neuralnetwork model for denoising includes: training the general neuralnetwork model using the one or more groups of second training data toobtain the personalized neural network model.
 33. The system of claim29, wherein the fourth equivalent dose level is in a range from 5% to40% of the third equivalent dose level.
 34. The system of claim 33,wherein the third image data includes planning image data of the targetsubject used to design a radiation treatment plan for the targetsubject, and the fourth image data includes guiding image data of thetarget subject used to guide implementation of the radiation treatmentplan.
 35. The system of claim 29, wherein the third image data and thefourth image data are acquired by different devices.
 36. The system ofclaim 35, wherein the third image data is acquired by a computedtomography (CT), and the fourth image data is acquired by an imagingdevice of an image guided radiation therapy (IGRT) device.
 37. Thesystem of claim 29, wherein the first equivalent dose level is in arange from 5% to 40% of the second equivalent dose level.
 38. The systemof claim 29, wherein the first equivalent dose level is in a range from5% to 20% of the second equivalent dose level.
 39. The system of claim29, wherein the first equivalent dose level is in a range from 5% to 10%of the second equivalent dose level.
 40. The system of claim 29, whereinthe first image data and the second image data are acquired by acomputed tomography (CT) device, and a ratio of the first equivalentdose level to the second equivalent dose level is equal to 1:7 or 1:5.41. The system of claim 29, wherein the first image data and the secondimage data are acquired by a cone beam computed tomography (CBCT), and aratio of the first equivalent dose level to the second equivalent doselevel is equal to 1:3 or 1:2.
 42. The system of claim 29, wherein thethird image data in each group of the one or more groups of secondtraining data relating to the target subject is same.
 43. The system ofclaim 29, wherein the second image data includes planning image data ofthe subject used to design a radiation treatment plan for the subject,and the first image data includes guiding image data of the subject usedto guide implementation of the radiation treatment plan.
 44. The systemof claim 29, wherein the second image data includes a fused image of thesubject generated by fusing at least two of guiding images of thesubject that are used to guide implementation of a radiation treatmentplan of the subject.
 45. The system of claim 29, wherein the secondimage data includes a fused image of the subject generated by fusing aplanning image of the subject that is used to design a radiationtreatment plan for the subject and at least one of guiding images of thesubject that are used to guide implementation of the radiation treatmentplan of the subject.
 46. The system of claim 29, further comprisingpreprocessing at least one of the first image data, the second imagedata, the third image data, or the fourth image data.
 47. A method,implemented on a computing device having at least one processor and atleast one computer-readable storage medium, the method comprising:obtaining multiple groups of first training data, each of the multiplegroups of first training data including first image data correspondingto a first equivalent dose level and second image data corresponding toa second equivalent dose level higher than the first equivalent doselevel, the first image data and the second image data including arepresentation of a same portion of a subject; determining, based on themultiple groups of first training data, a general neural network model;obtaining one or more groups of second training data relating to atarget subject, each of the one or more groups of second training dataincluding third image data corresponding to a third equivalent doselevel and fourth image data corresponding to a fourth equivalent doselevel lower than the third equivalent dose level; and determining, basedon the multiple groups of second training data and the general neuralnetwork model for denoising, a personalized neural network model for thetarget subject.
 48. A non-transitory computer readable medium,comprising: instructions being executed by at least one processor,causing the at least one processor to implement a method, comprising:obtaining multiple groups of first training data, each of the multiplegroups of first training data including first image data correspondingto a first equivalent dose level and second image data corresponding toa second equivalent dose level higher than the first equivalent doselevel, the first image data and the second image data including arepresentation of a same portion of a subject; determining, based on themultiple groups of first training data, a general neural network model;obtaining one or more groups of second training data relating to atarget subject, each of the one or more groups of second training dataincluding third image data corresponding to a third equivalent doselevel and fourth image data corresponding to a fourth equivalent doselevel lower than the third equivalent dose level; and determining, basedon the multiple groups of second training data and the general neuralnetwork model for denoising, a personalized neural network model for thetarget subject.